CAREER: Advancing Viral RNA Structure Prediction

RNA病毒是自然界最成功的自组装纳米系统之一。这个职业奖项目旨在确定病毒颗粒内的核糖核酸(RNA)的构象。自从50年前研究第一批病毒晶体以来，RNA的构象一直难以捉摸。病毒RNA在被复制、翻译和封装时会改变构象。病毒RNA序列编码病毒RNA、病毒蛋白、小干扰RNA(SiRNA)和宿主小RNA的靶点的结构和功能。随着基因组测序项目产生越来越大量的数据，对在结构水平上解释基因组序列信息的工具的需求变得越来越迫切。这一研究将为更好地理解被包裹的病毒RNA的结构，提高从序列预测RNA结构的水平，从而阐明病毒RNA结构与功能的动态关系提供基础知识。卫星烟草花叶病毒(STMV)将作为一个小模型系统进行研究，以改进病毒RNA结构预测。STMV颗粒的出色结晶学数据揭示了RNA螺旋在病毒颗粒中的位置和长度。由于缺乏RNA二级结构信息，限制了对STMV RNA结构的完整建模。目前的程序预测在一个很小的自由能范围内存在大量和多样化的RNA二级结构，但最低能量结构与结晶学数据不一致。STMV RNA的二级结构将通过化学修饰试剂和定点诱变进行进一步的探索。预测程序将被修改，以包括全球限制，如螺旋的数量和长度，并更有效地搜索低能量结构。RNA二级结构基序的热力学参数构成了大多数RNA结构预测程序的基础，并且对于基于自由能最小化的预测是必不可少的。虽然在RNA螺旋末端经常出现连续的末端非正则对，但该基序的热力学参数尚未被探索。测量RNA的热力学参数也为本科生提供了一个很好的机会将物理化学和生物化学课程中的概念应用到实际的生物学问题中，并为改进RNA热力学参数的持续努力做出贡献。这项研究的更广泛影响包括通过互联网改进RNA研究界广泛使用的热力学参数和RNA折叠算法。数以千计的科学家使用这些RNA预测程序来分析数据，产生关于RNA结构-功能关系的假设，或者设计siRNA策略。该项目为生物、化学和计算机科学领域的本科生、研究生和博士后提供长期的研究和教育机会。鼓励学生通过与当地图书馆的研究项目有关的科学展示，培养向公众传播科学的技能。

RNA viruses are one of Nature's most successful self-assembling nanosystems. This Career Award project aims to determine the conformation of ribonucleic acid (RNA) inside viral particles. The conformation of the RNA has remained elusive since the first crystals of viruses were studied 50 years ago. Viral RNA changes conformation as the RNA is replicated, translated, and encapsidated. A viral RNA sequence encodes the structure and the function of the viral RNA, viral proteins, small interfering RNA (siRNA), and target sites for host small RNA. As genome sequencing projects produce increasingly vast amounts of data, the need for tools to interpret genomic sequence information at a structural level becomes increasingly urgent. This research will provide fundamental knowledge to better understand the structure of encapsidated viral RNA, improve predictions of RNA structure from sequence, and thus elucidate dynamic viral RNA structure-function relationships. Satellite tobacco mosaic virus (STMV) will be studied as a small model system to improve viral RNA structure prediction. Excellent crystallographic data for STMV particles has revealed the position and length of RNA helices within the viral particle. A lack of RNA secondary structure information limits the complete modeling of STMV RNA structure. Current programs predict a large number and variety of RNA secondary structures within a small free-energy range, but the lowest energy structures are inconsistent with the crystallography data. The STMV RNA secondary structure will be further probed with chemical modification reagents and site-directed mutagenesis. Prediction programs will be modified to include global restraints, such as the number and length of helices, and to search low energy structures more efficiently. Thermodynamic parameters for RNA secondary structure motifs form the basis for most RNA structure prediction programs and are essential for predictions based on free energy minimization. Although consecutive terminal noncanonical pairs at the ends of RNA helices commonly occur, the thermodynamic parameters for this motif have not been explored. Measuring thermodynamic parameters for RNA also provides an excellent opportunity for undergraduates to apply concepts from physical chemistry and biochemistry courses to a practical biological problem and contribute to an ongoing effort to improve RNA thermodynamic parameters.The broader impacts of this research include the improvement of the thermodynamic parameters and RNA folding algorithms that are widely used by the RNA research community through the internet. Thousands of scientists use these RNA prediction programs to analyze data, generate hypotheses about RNA structure-function relationships, or design siRNA strategies. The project provides long-term research and educational opportunities for undergraduate, graduate and post doctoral students at the interface of biology, chemistry, and computer science. Students are encouraged to develop skills in the communication of science to the general public through science based displays relating to the research project at the local library.